A phase change memory cell is an electronic device comprising a phase change material arranged between two electrodes. The phase change material can reversibly pass from an amorphous phase, characterized by a high electrical resistivity, to a crystalline phase, characterized by a low electrical resistivity. The overall resistance of such a memory cell thus depends on the proportion of each of the phases within the phase change material. The transition from the amorphous state to the crystalline state, and vice versa, takes place by applying to the memory cell electrical pulses adapted for each transition from one state to the other.
The performances of phase change memory cells from the point of view of integration capacity, endurance and electrical consumption make them promising candidates for producing artificial synapses intended to connect artificial neurons to each other. It is thereby possible to produce an artificial neural network inspired by the working of the human brain. After a learning phase during which synaptic connections between neurons are created and modified according to a learning rule, the neural network may be used for the detection and the classification of objects and patterns.
A learning rule conventionally used by neural networks is the so-called spike timing dependent plasticity (STDP) rule. It is a biologically inspired rule, the objective of which is to reproduce the learning and memorization mechanisms of biological neurons and synapses.
Generally speaking, synaptic plasticity is the capacity of synapses to modify their force as a function of the use that is made thereof. Synaptic plasticity is based on two phenomena, namely long-term potentiation (LTP) which corresponds to an increase in the synaptic force, and long-term depression (LTD) which corresponds to a decrease in the synaptic force.
To implement the STDP rule, it is necessary that the resistance of the memory cells that model the artificial synapses of the neural network can vary progressively, not just in the sense of increase to simulate long-term depression but also in the sense of decrease to simulate long-term potentiation. To do so, several methods are known in the prior art.
The document KUZUM D. et al., “Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing”, Nano Letters 2012, 12 (5), pages 2179-2186, proposes a first method consisting in adapting the amplitude of the pulses applied to the memory cell as a function of the desired change of state. The progressive increase of the resistance of the memory cell is for example obtained by using pulses of which the amplitude increases after each pulse. As for the progressive decrease of the resistance of the memory cell, it may be obtained by using staircase shaped pulses, each step corresponding to an amplitude value for which the pulses are repeated a certain number of times. A drawback of this method is that its implementation is complex from the pulse programming viewpoint.
The document SURI M. et al., “Phase Change Memory as Synapse for Ultra-Dense Neuromorphic Systems: Application to Complex Visual Pattern Extraction”, IEEE International Electron Devices Meeting, IEDM 2011, pages 4.4.1-4.4.4, proposes a second method in the form of a circuit solution consisting in using two identical phase change memory cells to form a single synapse. This method is based on the fact that a progressive crystallization of the phase change material may be obtained by applying identical pulses to the memory cell. Thus, by direct mounting a first cell and reverse mounting the second cell, the effects of long-term potentiation may be reproduced by the progressive crystallization of the first memory cell and the effects of long-term depression may be reproduced by the progressive crystallization of the second memory cell. In this case, the programming of the pulses is simplified. However, a drawback of this method it that it requires operations of refreshing the state of the memory cells which are both long and costly from the electrical consumption viewpoint. In addition, this method doubles the number of memory cells necessary to form the synapses of the neural network, which reduces the integration capacity.